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Abstract Background The Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses. Methods This paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference. Results The model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15–0.29) and transmission rate of 5.0 (95% CI 2.4–6.6) for the Omicron variant. Conclusions The study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses.

Abstract In this report, we present OLAF-Seq, a novel strategy to construct a long-read sequencing library such that adjacent fragments are linked with end-terminal duplications. We use the CRISPR-Cas9 nickase enzyme and a pool of multiple sgRNAs to perform non-random fragmentation of targeted long DNA molecules (> 300kb) into smaller library-sized fragments (about 20 kbp) in a manner so as to retain physical linkage information (up to 1000 bp) between adjacent fragments. DNA molecules targeted for fragmentation are preferentially ligated with adaptors for sequencing, so this method can enrich targeted regions while taking advantage of the long-read sequencing platforms. This enables the sequencing of target regions with significantly lower total coverage, and the genome sequence within linker regions provides information for assembly and phasing. We demonstrated the validity and efficacy of the method first using phage and then by sequencing a panel of 100 full-length cancer-related genes (including both exons and introns) in the human genome. When the designed linkers contained heterozygous genetic variants, long haplotypes could be established. This sequencing strategy can be readily applied in both PacBio and Oxford Nanopore platforms for both long and short genes with an easy protocol. This economically viable approach is useful for targeted enrichment of hundreds of target genomic regions and where long no-gap contigs need deep sequencing.

Abstract Newly created arteriovenous fistulas (AVFs) often fail to mature for dialysis use due to disturbed blood flow at and near the AVF anastomosis. The disturbed flow inhibits the endothelial nitric oxide synthase (NOS3) pathway, thus decreasing the production of nitric oxide, a vasodilator. Previously, our group reported that NOS3 expression levels affect AVF lumen size in a mouse model. In this study, we performed MRI-based computational fluid dynamics simulations to investigate the hemodynamical parameters (velocity, wall shear stress (WSS), and vorticity) in a mouse AVF model at day 7 and day 21 post-AVF creation using three NOS3 strains: overexpression (OE), knockout (KO), and wild-type (WT) control. This study is the first to reveal hemodynamics over time in mouse AVFs, consider spatial heterogeneity along the vein, and reveal the effect of NOS3 on the natural history of mouse AVF hemodynamics. From day 7 to day 21, OE has smoother streamlines and had significantly lower vorticity and WSS than WT and KO, suggesting that WSS was attempting to return to pre-surgery baseline, respectively. Our results conclude that the overexpression of NOS3 leads to desired optimal hemodynamics during AVF remodeling. Future studies can investigate enhancing the NOS3 pathway to improve AVF development.

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available.Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data.Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%–70% of real human stick falls up to 2.35 s in advance.Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

Objectives External control arms (ECAs) provide useful comparisons in clinical trials when randomised control arms are limited or not feasible. We conducted a systematic review to summarise applications of ECAs in trials of immune-mediated inflammatory diseases (IMIDs).Design Systematic review with an appraisal of ECA source quality rated across five domains (data collection, study populations, outcome definitions, reliability and comprehensiveness of the dataset, and other potential limitations) as high, low or unclear quality.Data sources Embase, Medline and Cochrane Central Register of Controlled Trial were searched through to 12 September 2023.Eligibility criteria Eligible studies were single-arm or randomised controlled trials (RCTs) of inflammatory bowel disease, pouchitis, rheumatoid arthritis, juvenile idiopathic arthritis, ankylosing spondylitis, psoriatic arthritis, psoriasis and atopic dermatitis in which an ECA was used as the comparator.Data extraction and synthesis Two authors independently screened the search results in duplicate. The characteristics of included studies, external data source(s), outcomes and statistical methods were recorded, and the quality of the ECA data source was assessed by two independent authors.Results Forty-three studies met the inclusion criteria (inflammatory bowel disease: 16, pouchitis: 1, rheumatoid arthritis: 12, juvenile idiopathic arthritis: 1, ankylosing spondylitis: 5, psoriasis: 3, multiple indications: 4). The majority of these trials were single-arm (33/43) and enrolled adult patients (34/43). All included studies used a historical control rather than a contemporaneous ECA. In RCTs, ECAs were most often derived from the placebo arm of another RCT (6/10). In single-arm trials, historical case series were the most common ECA source (19/33). Most studies (31/43) did not employ a statistical approach to generate the ECA from historical data.Conclusions Standardised ECA methodology and reporting conventions are lacking for IMIDs trials. The establishment of ECA reporting guidelines may enhance the rigour and transparency of future research.

ABSTRACT Patients with chronic obstructive pulmonary disease (COPD) benefit from the immunomodulatory effect of azithromycin, but long-term administration may alter colonizing bacteria. Our goal was to identify changes in Haemophilus influenzae and Haemophilus parainfluenzae during azithromycin treatment. Fifteen patients were followed while receiving prolonged azithromycin treatment (Hospital Universitari de Bellvitge, Spain). Four patients (P02, P08, P11, and P13) were persistently colonized by H. influenzae for at least 3 months and two (P04 and P11) by H. parainfluenzae. Isolates from these patients (53 H. influenzae and 18 H. parainfluenzae) were included to identify, by whole-genome sequencing, antimicrobial resistance changes and genetic variation accumulated during persistent colonization. All persistent lineages isolated before treatment were azithromycin-susceptible but developed resistance within the first months, apart from those belonging to P02, who discontinued the treatment. H. influenzae isolates from P08-ST107 acquired mutations in 23S rRNA, and those from P11-ST2480 and P13-ST165 had changes in L4 and L22. In H. parainfluenzae, P04 persistent isolates acquired changes in rlmC, and P11 carried genes encoding MefE/MsrD efflux pumps in an integrative conjugative element, which was also identified in H. influenzae P11-ST147. Other genetic variation occurred in genes associated with cell wall and inorganic ion metabolism. Persistent H. influenzae strains all showed changes in licA and hgpB genes. Other genes (lex1, lic3A, hgpC, and fadL) had variation in multiple lineages. Furthermore, persistent strains showed loss, acquisition, or genetic changes in prophage-associated regions. Long-term azithromycin therapy results in macrolide resistance, as well as genetic changes that likely favor bacterial adaptation during persistent respiratory colonization. IMPORTANCE The immunomodulatory properties of azithromycin reduce the frequency of exacerbations and improve the quality of life of COPD patients. However, long-term administration may alter the respiratory microbiota, such as Haemophilus influenzae, an opportunistic respiratory colonizing bacteria that play an important role in exacerbations. This study contributes to a better understanding of COPD progression by characterizing the clinical evolution of H. influenzae in a cohort of patients with prolonged azithromycin treatment. The emergence of macrolide resistance during the first months, combined with the role of Haemophilus parainfluenzae as a reservoir and source of resistance dissemination, is a cause for concern that may lead to therapeutic failure. Furthermore, genetic variations in cell wall and inorganic ion metabolism coding genes likely favor bacterial adaptation to host selective pressures. Therefore, the bacterial pathoadaptive evolution in these severe COPD patients raise our awareness of the possible spread of macrolide resistance and selection of host-adapted clones.

In our community-based prospective cohort study in young children, we observed a significant increase in pneumococcal serotype 35B nasopharyngeal (NP) commensal colonization during the 2011–2014 timeframe, but these strains were not associated with disease. Beginning in 2015 and continuing through to the present, the serotype 35B virulence changed, and it became the dominant bacteria isolated and associated with pneumococcal acute otitis-media (AOM) in our cohort. We performed comparative analyses of 250 35B isolates obtained from 140 children collected between 2006 and 2019. Changes in prevalence, clonal-complex composition, and antibiotic resistance were analyzed. Seventy-two (29%) of 35B isolates underwent whole-genome sequencing to investigate genomic changes associated with the shift in virulence that resulted in increased rates of 35B-associated AOM disease. 35B strains that were commensals and AOM disease-causing were mainly associated with sequence type (ST) 558. Antibiotic concentrations of β-lactams and ofloxacin necessary to inhibit growth of 35B strains rose significantly (2006–2019) (p

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

Summary: The genomes of naturally competent Pasteurellaceae and Neisseriaceae have many short uptake sequences (USS), which allow them to distinguish self-DNA from foreign DNA. To fully characterize this preference we developed genome-wide maps of DNA uptake using both a sequence-based computational model and genomic DNA that had been sequenced after uptake by and recovery from competent Haemophilus influenzae cells. When DNA fragments were shorter than the average USS spacing of ∼1,000 bp, sharp peaks of uptake were centered at USS and separated by valleys with 1000-fold lower uptake. Long DNA fragments (1.5–17 kb) gave much less variation, with 90% of positions having uptake within 2-fold of the mean. All detectable uptake biases arose from sequences that fit the USS uptake motif. Simulated competition predicted that, in its respiratory tract environment, H. influenzae will efficiently take up its own DNA even when human DNA is present in 100-fold excess.

Abstract Background Pan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences’ (PacBio’s) long-read technology in particular suffers from high error rates per base. Herein, we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data. Results Analysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity with regard to taxonomic classification. Examination of a 250-plus species mock community demonstrated correct species-level classification of > 90% of taxa, and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes) was the predominant species throughout, but was found at distinct relative abundances by site. Conclusions Our microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt) overcomes deficits of standard marker gene-based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.

Natural competence allows bacteria to respond to environmental and nutritional cues by taking up free DNA from their surroundings, thus gaining both nutrients and genetic information. In the Gram-negative bacterium Haemophilus influenzae, the genes needed for DNA uptake are induced by the CRP and Sxy transcription factors in response to lack of preferred carbon sources and nucleotide precursors. Here we show that one of these genes, HI0659, encodes the antitoxin of a competence-regulated toxin-antitoxin operon ('toxTA'), likely acquired by horizontal gene transfer from a Streptococcus species. Deletion of the putative toxin (HI0660) restores uptake to the antitoxin mutant. The full toxTA operon was present in only 17 of the 181 strains we examined; complete deletion was seen in 22 strains and deletions removing parts of the toxin gene in 142 others. In addition to the expected Sxy- and CRP-dependent-competence promoter, HI0659/660 transcript analysis using RNA-seq identified an internal antitoxin-repressed promoter whose transcription starts within toxT and will yield nonfunctional protein. We propose that the most likely effect of unopposed toxin expression is non-specific cleavage of mRNAs and arrest or death of competent cells in the culture. Although the high frequency of toxT and toxTA deletions suggests that this competence-regulated toxin-antitoxin system may be mildly deleterious, it could also facilitate downregulation of protein synthesis and recycling of nucleotides under starvation conditions. Although our analyses were focused on the effects of toxTA, the RNA-seq dataset will be a useful resource for further investigations into competence regulation.

Respiratory viral infections contribute substantially to global infant losses and disproportionately affect preterm neonates. Using our previously established neonatal murine model of influenza infection, we demonstrate that three-day old mice are exceptionally sensitive to influenza virus infection and exhibit high mortality and viral load. Intranasal pre- and post-treatment of neonatal mice with Lactobacillus rhamnosus GG (LGG), an immune modulator in respiratory viral infection of adult mice and human preterm neonates, considerably improves neonatal mice survival after influenza virus infection. We determine that both live and heat-killed intranasal LGG are equally efficacious in protection of neonates. Early in influenza infection, neonatal transcriptional responses in the lung are delayed compared to adults. These responses increase by 24 hours post-infection, demonstrating a delay in the kinetics of the neonatal anti-viral response. LGG pretreatment improves immune gene transcriptional responses during early infection and specifically upregulates type I IFN pathways. This is critical for protection, as neonatal mice intranasally pre-treated with IFNβ before influenza virus infection are also protected. Using transgenic mice, we demonstrate that the protective effect of LGG is mediated through a MyD88-dependent mechanism, specifically via TLR4. LGG can improve both early control of virus and transcriptional responsiveness and could serve as a simple and safe intervention to protect neonates.

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using "topics" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.

ABSTRACT Tracking bacterial evolution during chronic infection provides insights into how host selection pressures shape bacterial genomes. The human-restricted opportunistic pathogen nontypeable Haemophilus influenzae (NTHi) infects the lower airways of patients suffering chronic obstructive pulmonary disease (COPD) and contributes to disease progression. To identify bacterial genetic variation associated with bacterial adaptation to the COPD lung, we sequenced the genomes of 92 isolates collected from the sputum of 13 COPD patients over 1 to 9 years. Individuals were colonized by distinct clonal types (CTs) over time, but the same CT was often reisolated at a later time or found in different patients. Although genomes from the same CT were nearly identical, intra-CT variation due to mutation and recombination occurred. Recurrent mutations in several genes were likely involved in COPD lung adaptation. Notably, nearly a third of CTs were polymorphic for null alleles of ompP1 (also called fadL), which encodes a bifunctional membrane protein that both binds the human carcinoembryonic antigen-related cell adhesion molecule 1 (hCEACAM1) receptor and imports long-chain fatty acids (LCFAs). Our computational studies provide plausible three-dimensional models for FadL’s interaction with hCEACAM1 and LCFA binding. We show that recurrent fadL mutations are likely a case of antagonistic pleiotropy, since loss of FadL reduces NTHi’s ability to infect epithelia but also increases its resistance to bactericidal LCFAs enriched within the COPD lung. Supporting this interpretation, truncated fadL alleles are common in publicly available NTHi genomes isolated from the lower airway tract but rare in others. These results shed light on molecular mechanisms of bacterial pathoadaptation and guide future research toward developing novel COPD therapeutics. IMPORTANCE Nontypeable Haemophilus influenzae is an important pathogen in patients with chronic obstructive pulmonary disease (COPD). To elucidate the bacterial pathways undergoing in vivo evolutionary adaptation, we compared bacterial genomes collected over time from 13 COPD patients and identified recurrent genetic changes arising in independent bacterial lineages colonizing different patients. Besides finding changes in phase-variable genes, we found recurrent loss-of-function mutations in the ompP1 (fadL) gene. We show that loss of OmpP1/FadL function reduces this bacterium’s ability to infect cells via the hCEACAM1 epithelial receptor but also increases its resistance to bactericidal fatty acids enriched within the COPD lung, suggesting a case of antagonistic pleiotropy that restricts ΔfadL strains’ niche. These results show how H. influenzae adapts to host-generated inflammatory mediators in the COPD airways.

Many bacterial species actively take up and recombine homologous DNA into their genomes, called natural competence, a trait that offers a means to identify the genetic basis of naturally occurring phenotypic variation. Here, we describe "transformed recombinant enrichment profiling" (TREP), in which natural transformation is used to generate complex pools of recombinants, phenotypic selection is used to enrich for specific recombinants, and deep sequencing is used to survey for the genetic variation responsible. We applied TREP to investigate the genetic architecture of intracellular invasion by the human pathogen Haemophilus influenzae, a trait implicated in persistence during chronic infection. TREP identified the HMW1 adhesin as a crucial factor. Natural transformation of the hmw1 operon from a clinical isolate (86-028NP) into a laboratory isolate that lacks it (Rd KW20) resulted in ~1,000-fold increased invasion into airway epithelial cells. When a distinct recipient (Hi375, already possessing hmw1 and its paralog hmw2) was transformed by the same donor, allelic replacement of hmw2AHi375 by hmw1A86-028NP resulted in a ~100-fold increased intracellular invasion rate. The specific role of hmw1A86-028NP was confirmed by mutant and western blot analyses. Bacterial self-aggregation and adherence to airway cells were also increased in recombinants, suggesting that the high invasiveness induced by hmw1A86-028NP might be a consequence of these phenotypes. However, immunofluorescence results found that intracellular hmw1A86-028NP bacteria likely invaded as groups, instead of as individual bacterial cells, indicating an emergent invasion-specific consequence of hmw1A-mediated self-aggregation.

Evolutionary divergence of the mitochondrial genome has given rise to distinct haplogroups. These haplogroups have arisen in specific geographical locations and are responsible for subtle functional changes in the mitochondria that may provide an evolutionary advantage in a given environment. Based on these functional differences, haplogroups could define disease susceptibility in chronic settings. In this study, we undertook a detailed neuropsychological analysis of a cohort of long-term HIV-1-infected individuals in conjunction with sequencing of their mitochondrial genomes. Stepwise regression analysis showed that the best model for predicting both working memory and declarative memory were age and years since diagnosis. In contrast, years since diagnosis and sub-haplogroup were significantly predictive of psychomotor speed. Consistent with this, patients with haplogroup L3e obtained better scores on psychomotor speed and dexterity tasks when compared to the remainder of the cohort, suggesting that this haplogroup provides a protective advantage when faced with the combined stress of HIV-1 infection and long-term antiretroviral therapies. Differential performance on declarative memory tasks was noted for individuals with other sub-L haplogroups, but these differences were not as robust as the association between L3e and psychomotor speed and dexterity tasks. This work provides evidence that mitochondrial haplogroup is related to neuropsychological test performance among patients in chronic disease settings such as HIV-1 infection.

The purpose of this research is to develop a new torque vectoring differential (TVD) for vehicle applications and investigate its effect on vehicle dynamic control. TVD is a technology that is able to distribute the engine torque to the left and right driving wheels at different ratios so that the yaw motion control can be realized. Attention has been paid to this technology in recent years because of its potential to improve the vehicle performance and driving safety. In this study, a new TVD design with a Ravigneaux gearset was developed. This new design is able to use only one pair of gearsets to generate two different speed ratios, and the weight and volume of the system can be reduced. To execute the research, current TVD designs were analyzed and their design principles were clarified. Next, a new TVD design with Ravigneaux gearset was proposed. Then the connecting manner and the gear ratio of the Ravigneaux gearset were discussed. The dynamic equation of the system was then derived and the operation of the system was simulated in a MATLAB program. Further simulation was performed with a vehicle dynamic model in SimulationX to demonstrate the effect of the new system. The results of this study show the potential of building a new TVD with a Ravigneaux gearset and can be helpful for further system development.

Nontypable Haemophilus influenzae (NTHi) has emerged as an important opportunistic pathogen causing infection in adults suffering obstructive lung diseases. Existing evidence associates chronic infection by NTHi to the progression of the chronic respiratory disease, but specific features of NTHi associated with persistence have not been comprehensively addressed. To provide clues about adaptive strategies adopted by NTHi during persistent infection, we compared sequential persistent isolates with newly acquired isolates in sputa from six patients with chronic obstructive lung disease. Pulse field gel electrophoresis (PFGE) identified three patients with consecutive persistent strains and three with new strains. Phenotypic characterisation included infection of respiratory epithelial cells, bacterial self-aggregation, biofilm formation and resistance to antimicrobial peptides (AMP). Persistent isolates differed from new strains in showing low epithelial adhesion and inability to form biofilms when grown under continuous-flow culture conditions in microfermenters. Self-aggregation clustered the strains by patient, not by persistence. Increasing resistance to AMPs was observed for each series of persistent isolates; this was not associated with lipooligosaccharide decoration with phosphorylcholine or with lipid A acylation. Variation was further analyzed for the series of three persistent isolates recovered from patient 1. These isolates displayed comparable growth rate, natural transformation frequency and murine pulmonary infection. Genome sequencing of these three isolates revealed sequential acquisition of single-nucleotide variants in the AMP permease sapC, the heme acquisition systems hgpB, hgpC, hup and hxuC, the 3-deoxy-D-manno-octulosonic acid kinase kdkA, the long-chain fatty acid transporter ompP1, and the phosphoribosylamine glycine ligase purD. Collectively, we frame a range of pathogenic traits and a repertoire of genetic variants in the context of persistent infection by NTHi.

Telomeres are critical in maintaining genomic stability. Genetic variants in telomere pathway genes may affect telomere and telomerase function, and subsequently cancer risk. We evaluated 126 SNPs from 10 genes related to telomere regulation in relation to bladder cancer risk. Five SNPs, 4 from TEP1 gene and 1 from PINX1 gene, were found to be highly significant (P

Many bacteria are able to efficiently bind and take up double-stranded DNA fragments, and the resulting natural transformation shapes bacterial genomes, transmits antibiotic resistance, and allows escape from immune surveillance. The genomes of many competent pathogens show evidence of extensive historical recombination between lineages, but the actual recombination events have not been well characterized. We used DNA from a clinical isolate of Haemophilus influenzae to transform competent cells of a laboratory strain. To identify which of the ~40,000 polymorphic differences had recombined into the genomes of four transformed clones, their genomes and their donor and recipient parents were deep sequenced to high coverage. Each clone was found to contain ~1000 donor polymorphisms in 3-6 contiguous runs (8.1±4.5 kb in length) that collectively comprised ~1-3% of each transformed chromosome. Seven donor-specific insertions and deletions were also acquired as parts of larger donor segments, but the presence of other structural variation flanking 12 of 32 recombination breakpoints suggested that these often disrupt the progress of recombination events. This is the first genome-wide analysis of chromosomes directly transformed with DNA from a divergent genotype, connecting experimental studies of transformation with the high levels of natural genetic variation found in isolates of the same species.

Maf1 is the master repressor of RNA polymerase III responsible for transcription of tRNAs and 5S rRNAs. Maf1 is negatively regulated via phosphorylation by the mTOR pathway, which governs protein synthesis, growth control, and lifespan regulation in response to nutrient availability. Inhibiting the mTOR pathway extends lifespan in various organisms. However, the downstream effectors for the regulation of cell homeostasis that are critical to lifespan extension remain elusive. Here we show that fission yeast Maf1 is required for lifespan extension. Maf1's function in tRNA repression is inhibited by mTOR-dependent phosphorylation, whereas Maf1 is activated via dephosphorylation by protein phosphatase complexes, PP4 and PP2A. Mutational analysis reveals that Maf1 phosphorylation status influences lifespan, which is correlated with elevated tRNA and protein synthesis levels in maf1∆ cells. However, mTOR downregulation, which negates protein synthesis, fails to rescue the short lifespan of maf1∆ cells, suggesting that elevated protein synthesis is not a cause of lifespan shortening in maf1∆ cells. Interestingly, maf1∆ cells accumulate DNA damage represented by formation of Rad52 DNA damage foci and Rad52 recruitment at tRNA genes. Loss of the Rad52 DNA repair protein further exacerbates the shortened lifespan of maf1∆ cells. Strikingly, PP4 deletion alleviates DNA damage and rescues the short lifespan of maf1∆ cells even though tRNA synthesis is increased in this condition, suggesting that elevated DNA damage is the major cause of lifespan shortening in maf1∆ cells. We propose that Maf1-dependent inhibition of tRNA synthesis controls fission yeast lifespan by preventing genomic instability that arises at tRNA genes.

Background: The Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses. Methods: This paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference. Results: The model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15–0.29) and transmission rate of 5.0 (95% CI 2.4–6.6) for the Omicron variant. Conclusions: The study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses. [ABSTRACT FROM AUTHOR]

Background The critical issues of sustained memory immunity following ebolavirus disease among long-term survivors are still unclear. Methods Here, we examine virus-specific immune and inflammatory responses following in vitro challengd in 12 Sudan virus (SUDV) long-term survivors from Uganda's 2000–2001 Gulu outbreak, 15 years after recovery. Total RNA from isolated SUDV-stimulated and unstimulated peripheral blood mononuclear cells was extracted and analyzed. Matched serum samples were also collected to determine SUDV IgG levels and functionality. Results We detected persistent humoral (58%, 7 of 12) and cellular (33%, 4 of 12) immune responses in SUDV long-term survivors and identified critical molecular mechanisms of innate and adaptive immunity. Gene expression in immune pathways, the interferon signaling system, antiviral defense response, and activation and regulation of T- and B-cell responses were observed. SUDV long-term survivors also maintained robust virus-specific IgG antibodies capable of polyfunctional responses, including neutralizing and innate Fc effector functions. Conclusions Data integration identified significant correlations among humoral and cellular immune responses and pinpointed a specific innate and adaptive gene expression signature associated with long-lasting immunity. This could help identify natural and vaccine correlates of protection against ebolavirus disease. [ABSTRACT FROM AUTHOR]